33 citations · 70 across the 5 of their papers we have counts for
6 papers
Passive learning to address nonstationarity in virtual flow metering applications
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
Steady-state process models are common in virtual flow meter applications due to low computational complexity, and low model development and maintenance cost. Nevertheless, the pre…
When is gray-box modeling advantageous for virtual flow metering?
M. Hotvedt, B. Grimstad, D. Ljungquist +1
Integration of physics and machine learning in virtual flow metering applications is known as gray-box modeling. The combination is believed to enhance multiphase flow rate predict…
On gray-box modeling for virtual flow metering
Mathilde Hotvedt, Bjarne Grimstad, Dag Ljungquist +1
A virtual flow meter (VFM) enables continuous prediction of flow rates in petroleum production systems. The predicted flow rates may aid the daily control and optimization of a pet…
Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study
Bjarne Grimstad, Mathilde Hotvedt, Anders T. Sandnes +2
Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells. Encouraging results and advantageous…
Identifiability and physical interpretability of hybrid, gray-box models -- a case study
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
Model identifiability concerns the uniqueness of uncertain model parameters to be estimated from available process data and is often thought of as a prerequisite for the physical i…
Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a Case Study
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
Virtual flow meters, mathematical models predicting production flow rates in petroleum assets, are useful aids in production monitoring and optimization. Mechanistic models based o…